PhD Thesis Defense

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TITLE:  Residual Life Prediction and Degradation-Based Control of Multi-Component Systems



The condition monitoring of multi-component systems utilizes multiple sensors to capture the functional condition of the systems, and allows the sensor information to be used to reason about the health information of the systems or components. This thesis focuses on modeling the relationship between multi-sensor information and component-level degradation, so as to prediction both system-level and component-level lifetimes. In addition, this thesis also investigates the dynamic control of component-level degradation so as to control the failure times of individual components based on real-time degradation monitoring.

The research topic that Chapter 3 focuses on is identifying component degradation signals from mixed sensor signals in order to predict component-level residual lives. Specifically, we are interested in modeling the degradation of systems that consist of two or more identical components operating under similar conditions. The key challenge here is that a defect in any of the components will excite the same defective frequency, which prevents an effective separation of the degradation signals of defective and non-defective components. To the best of our knowledge, no existing methodologies have investigated this research topic. In Chapter 3, we propose a two-stage vibration-based prognostic methodology for modeling the degradation processes of components with identical defective frequencies. The first stage incorporates the independent component analysis (ICA) to identify component vibration signals and reverse their original amplitude. The second stage consists of an adaptive prognostics method to predict component residual lives. In the simulated case study, we investigate the performance of the signal separation stage and that of the final residual-life prediction under different conditions. The simulation results show reasonable robustness of the methodology.

In Chapter 4, we focus on characterizing the interactive relationship between product quality degradation and tool wear in multistage manufacturing processes (MMPs), in which machine tools are considered as components and the product quality measurements are considered as condition monitoring information. Due to the sequential structure of MMPs, the degradation status of a tool affects the product quality current stage, which, on the other hand, may affect the degradation of tools at subsequent stages. To the best of our knowledge, although existing literature has modeled the impact of product quality on the tooling catastrophic failure, no published work has targeted on the impact of product quality on the actual process of tool wear. To address this research topic, we propose an high-dimensional stochastic differential equation model to capture the interaction relationship between the process of tool wear and product quality. We then leverage real-time quality measurements to on-line predict the residual life of the MMP as a system. In the simulation study, we conclude that our methodology consistently performs better than a benchmark methodology that does not consider the impact of product quality on the process of tool wear or utilize real-time quality measurements.

Chapter 5 explores a new research direction, which is the dynamic control of component-level degradation in the parallel multi-component system, in which each component operates simultaneously to achieve an engineering objective. This parallel configuration is usually designed with some level of redundancy, which means when a small portion of components fails to operate, the remaining components can still achieve the engineering objective by increasing their workloads up to the designed capacities. Consequently, if the component degradation can be controlled, we can achieve better utilization of the redundancy to ensure consistent system performance. To do this, Chapter 5 assumes that the degradation rate of a component is directly related to its workload and develops a strategy of dynamic workload adjustment in order to on-line control the degradation processes of individual components, and thus to control their failure times. The criterion of selecting the optimal workloads is to prevent the overlap of component failures. We conduct a simulated case study to evaluate the performance of our proposed methodology under different conditions.


  • Workflow Status:Published
  • Created By:Anita Race
  • Created:02/18/2015
  • Modified By:Fletcher Moore
  • Modified:04/13/2017



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